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Use of Cohort Information in the Design and Analysis of Case‐Control Studies

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  • BRYAN LANGHOLZ

Abstract

. Four case studies are presented to illustrate how information available on cohort members can be used to inform the control selection in epidemiologic case‐control studies. The basic framework is the nested case‐control paradigm and accompanying analysis methods. Emphasis is on development of intuition for choosing study design candidates, the form of the estimators, and extensions of the basic theory to solve design and analysis problems.

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  • Bryan Langholz, 2007. "Use of Cohort Information in the Design and Analysis of Case‐Control Studies," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 34(1), pages 120-136, March.
    • Handle: RePEc:bla:scjsta:v:34:y:2007:i:1:p:120-136
    DOI: 10.1111/j.1467-9469.2006.00548.x
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    Cited by:

    1. Mulugeta Gebregziabher & Bryan Langholz, 2010. "A Semiparametric Missing-Data-Induced Intensity Method for Missing Covariate Data in Individually Matched Case–Control Studies," Biometrics, The International Biometric Society, vol. 66(3), pages 845-854, September.
    2. Rakovski Cyril & Langholz Bryan, 2015. "A post-hoc Unweighted Analysis of Counter-Matched Case-Control Data," The International Journal of Biostatistics, De Gruyter, vol. 11(2), pages 223-232, November.
    3. Juha Karvanen, 2015. "Study Design in Causal Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(2), pages 361-377, June.
    4. Karvanen, Juha & Kulathinal, Sangita & Gasbarra, Dario, 2009. "Optimal designs to select individuals for genotyping conditional on observed binary or survival outcomes and non-genetic covariates," Computational Statistics & Data Analysis, Elsevier, vol. 53(5), pages 1782-1793, March.

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